Skewed log-stable model for natural images pixel block-variance

Authors:
Juan Ramón Troncoso-Pastoriza;Fernando Pérez-González
Affiliations:
Signal Theory and Communications Department, University of Vigo, Vigo, Spain;Signal Theory and Communications Department, University of Vigo, Vigo, Spain
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

This work presents a Log-stable model for natural images block-variance. Exponential and halfnormal distributions have been previously used to model block-variance, but they were employed to fit images for which the assumption of constant intra-block variance does not hold. We show that when this assumption holds, the Log-stable model yields a much better fit in an ML sense. We use a computationally efficient method for estimating the Log-stable parameters through the empirical Kullback-Leibler Divergence, which is asymptotically optimum in an ML sense, and show the validity of the lognormal distribution as an approximation with closed-form formulas for the ML parameter estimation.